code: def testQuantizesOutputsFromLayer(self):
      # TODO(pulkitb): Increase coverage by adding other output quantize layers
      # such as AveragePooling etc.
  
      layer = layers.ReLU()
      quantized_model = keras.Sequential(
          [
              QuantizeWrapper(
                  layers.ReLU(),
                  quantize_config=self.quantize_registry.get_quantize_config(
                      layer
                  ),
              )
          ]
      )
  
      model = keras.Sequential([layers.ReLU()])
  
      inputs = np.random.rand(1, 2, 1)
      expected_output = tf.quantization.fake_quant_with_min_max_vars(
          model.predict(inputs),
          -6.0,
          6.0,
          num_bits=8,
          narrow_range=False,
      )
      self.assertAllClose(
          expected_output, quantized_model.predict(inputs)
      )
  
file_path: model-optimization/tensorflow_model_optimization/python/core/quantization/keras/quantize_wrapper_test.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tensorflow_model_optimization.python.core.quantization.keras import quantize_aware_activation
  from tensorflow_model_optimization.python.core.quantization.keras import quantize_wrapper
  from tensorflow_model_optimization.python.core.quantization.keras.default_8bit import default_8bit_quantize_registry
label_desc: Use image node_id 4 for calling the QuantizeWrapperTest obj's underlying member method code with example usage: obj.testQuantizesOutputsFromLayer() without return types